Passenger data from the Titanic

The dataset contains information about the passengers of the RMS Titanic, which sank on April 15, 1912, after colliding with an iceberg. The data includes attributes such as travel class, age, gender, number of siblings/spouses aboard, number of parents/children aboard, ticket price, and embarkation point. The dataset also includes information on whether the passenger survived the disaster. The Titanic carried over 2,200 people, of which over 1,500 perished, making this disaster one of the most tragic in maritime history. Columns:

• pclass - Ticket class
• survived - Whether the passenger survived the disaster
• name - Passenger's name
• sex - Passenger's gender
• age - Passenger's age
• sibsp - Number of siblings/spouses aboard
• parch - Number of parents/children aboard
• ticket - Ticket number
• fare - Ticket price
• cabin - Cabin number
• embarked - Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)
• boat - Lifeboat number
• body - Body number (if the passenger did not survive and the body was recovered)
• home.dest - Destination

Titanic Disaster in Numbers - Mateusz Nowakowski

Explanatory Data Analysis

1. General Data Overview

The subject of the analysis is the Titanic disaster. There were a total of 2,212 people on the Titanic: 1,320 passengers and 892 crew members. We have almost a complete list of all passengers. It should be noted that the analysis concerns only passengers as we do not have data on the crew. We have a dataset consisting of 1,310 rows and 14 columns, compiled - as we can guess - after the disaster. Seven columns contain numbers and the other seven contain strings. At first glance, we can see that the dataset has quite a few gaps and the data will require processing.

Source: https://en.wikipedia.org/wiki/Sinking_of_the_Titanic

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1310 entries, 0 to 1309
Data columns (total 14 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   pclass     1309 non-null   float64
 1   survived   1309 non-null   float64
 2   name       1309 non-null   object 
 3   sex        1309 non-null   object 
 4   age        1046 non-null   float64
 5   sibsp      1309 non-null   float64
 6   parch      1309 non-null   float64
 7   ticket     1309 non-null   object 
 8   fare       1308 non-null   float64
 9   cabin      295 non-null    object 
 10  embarked   1307 non-null   object 
 11  boat       486 non-null    object 
 12  body       121 non-null    float64
 13  home.dest  745 non-null    object 
dtypes: float64(7), object(7)
memory usage: 143.4+ KB

2. Analysis of Missing Values

The dataset has many missing values. We will count and discuss them below.

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
1309	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

0

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
1308	3.0	0.0	Zimmerman, Mr. Leo	male	29.0	0.0	0.0	315082	7.875	NaN	S	NaN	NaN	NaN

Below we create a separate dataframe that contains the sum of all missing values for each column.

Let's discuss each column:

• columns 'pclass', 'survived', 'name', 'sex', 'sibsp', 'parch' and 'ticket' contain no missing values. This is very good news as these are key data for analysis
• in columns fare and embarked we see only single missing values - we will replace fare column with median while embarked column can be removed as it won't be needed for further analysis.
• columns 'cabin' and 'home destination' have many missing values but these are rather peripheral data so we will remove these two columns as they won't be needed for further analysis.
• column 'body' i.e. body number contains the most missing values at 90%. The very fact that there is no body number tells us that the body was most likely not found. In this case, therefore, lack of number is valuable information. We will not interfere with this data.
• in column 'boat' 823 records are missing. These are probably people who for various reasons did not make it onto a lifeboat. So we won't fill in missing data. The problem with this column, however, is that the same people have been assigned to more than one boat. For this reason, the column will require transformation.
• I left the 'age' column for last and here is a bit of a problem because this is very important information and we are missing 263 records or 20%. I see two options here: 1. any analysis of correlation with age will be conducted on a reduced sample. 2. we replace with mean or median and then we have a full sample and any age correlation studies will be somewhat distorted but will apply to all passengers. Option 2 seems better.

Missing Values and Percentage

	Missing Values	Percentage
pclass	0	0.000000
survived	0	0.000000
name	0	0.000000
sex	0	0.000000
age	263	20.091673
sibsp	0	0.000000
parch	0	0.000000
ticket	0	0.000000
fare	1	0.076394
cabin	1014	77.463713
embarked	2	0.152788
boat	823	62.872422
body	1188	90.756303
home.dest	564	43.086325

Data Transformation

Let me transform the data before we move on to analyzing individual variables. The data requires significant processing. I would like to change a few things before we start analysis and draw any conclusions.

Let's list all the changes:

• we remove columns 'ticket', 'embarked', 'cabin' and 'home.dest' because they won't be needed for what I want to show you.
• columns 'sibsp'(siblings/spouse) and 'parch'(parents/children) are combined into one column and transformed so that we only know if someone traveled with family (1.0) or alone (0.0) - that should be enough
• we fix the data in the 'boat' column so that one passenger is assigned to only one boat.
• missing values in the 'fare' column are replaced with median
• missing values in the 'age' column are replaced with median

	pclass	survived	name	sex	age	sibsp	parch	fare	boat	body
0	1.0	1.0	Allen, Miss. Elisabeth Walton	female	29.0	0.0	0.0	211.3375	2	NaN

	name	survived	sex	age	pclass	with family	fare	boat	body
902	Johnston, Mr. Andrew G	0.0	male	NaN	3.0	1.0	23.4500	NaN	NaN
636	Arnold-Franchi, Mrs. Josef (Josefine Franchi)	0.0	female	18.0	3.0	1.0	17.8000	NaN	NaN
463	Jefferys, Mr. Ernest Wilfred	0.0	male	22.0	2.0	1.0	31.5000	NaN	NaN
346	Botsford, Mr. William Hull	0.0	male	26.0	2.0	0.0	13.0000	NaN	NaN
143	Harder, Mr. George Achilles	1.0	male	25.0	1.0	1.0	55.4417	5	NaN

boat
13         39
C          38
15         37
14         33
4          31
10         29
5          27
3          26
9          25
11         25
16         23
8          23
7          23
D          20
6          20
12         19
2          13
A          11
B           9
1           5
5 7         2
C D         2
13 15       2
5 9         1
8 10        1
13 15 B     1
15 16       1
Name: count, dtype: int64

boat
13    42
C     40
15    38
14    33
4     31
5     30
10    29
3     26
9     25
11    25
8     24
16    23
7     23
D     20
6     20
12    19
2     13
A     11
B      9
1      5
Name: count, dtype: int64

	name	survived	sex	age	pclass	with family	fare	boat	body
334	Banfield, Mr. Frederick James	0.0	male	28.0	2.0	0.0	10.5000	NaN	NaN
1078	O'Dwyer, Miss. Ellen "Nellie"	1.0	female	28.0	3.0	0.0	7.8792	NaN	NaN
825	Goodwin, Master. Harold Victor	0.0	male	9.0	3.0	1.0	46.9000	NaN	NaN
1296	Wirz, Mr. Albert	0.0	male	27.0	3.0	0.0	8.6625	NaN	131.0
296	Thayer, Mrs. John Borland (Marian Longstreth M...	1.0	female	39.0	1.0	1.0	110.8833	4	NaN

name              0
survived          0
sex               0
age               0
pclass            0
with family       0
fare              0
boat            823
body           1188
dtype: int64

4. Single Variable Analysis

Now that we have properly processed our dataframe, we will answer basic questions about individual variables:

1. 'survived' - How many people survived the disaster?
2. 'sex' - How many women and men were there?
3. 'age' - Who were the youngest and oldest passengers and what was the average age?
4. 'pclass' - How many people traveled in each class?
5. 'with family' - How many people traveled alone versus with family?
6. 'fare' - What was the cheapest and most expensive ticket, and what was the average and median ticket price?
7. 'boat' - How many lifeboats were there and how many passengers were assigned to each lifeboat?
8. 'body' - How many passengers who did not survive were assigned a body number?

Number and Percentage of Survivors

	Survivors	Percentage
survived
0.000000	809	62.000000
1.000000	500	38.000000

Number and Percentage of Passengers by Gender

	Gender Count	Percentage
sex
male	843	64.000000
female	466	36.000000

Youngest, Oldest, Average and Median Age

	age
min	0.166700
mean	29.503183
median	28.000000
max	80.000000

Number and Percentage of Passengers in Each Class

	Class Count	Percentage
pclass
3.000000	709	54.000000
1.000000	323	25.000000
2.000000	277	21.000000

Number and Percentage of Passengers Traveling Alone vs. With Family

	Family Count	Percentage
with family
0.000000	790	60.000000
1.000000	519	40.000000

Cheapest, Most Expensive, Average and Median Ticket Price

	fare
min	3.170800
mean	33.718995
median	14.500000
max	512.329200

Number of Passengers in Each Lifeboat

	People In the boat
boat
13	42
C	40
15	38
14	33
4	31
5	30
10	29
3	26
9	25
11	25
8	24
16	23
7	23
D	20
6	20
12	19
2	13
A	11
B	9
1	5

People In the boat    486
dtype: int64

How many passengers who did not survive the disaster were assigned a body number?

	Total Not Survived	Not Survived With Body Number	Percentage
0	809	121	14.956737

Summary of the analysis of individual variables

After an initial analysis of the variables, we learn that:

1. 500 passengers survived the disaster, which represents only 38% of all passengers.
2. Among the passengers, there were 843 men and 466 women, accounting for 64% and 36%, respectively.
3. The youngest passenger was only two months old, the oldest was 80 years old, the average age was nearly 30 years, and the median age was 28 years.
4. 323 (25%) passengers traveled in 1st class, 272 (21%) in 2nd class, and as many as 709 (54%) in 3rd class.
5. 790 (60%) passengers traveled without family members, while 519 (40%) traveled with family.
6. The cheapest ticket among the passengers was free*, the cheapest non-free ticket cost 3.14 GBP, the average ticket price was 33.28 GBP, the median was 14.45 GBP, and the most expensive ticket was 512.32 GBP.
7. There were 20 lifeboats on the Titanic, which collectively accommodated 486 survivors.
8. Only 121 out of 809 passengers who did not survive the disaster were assigned a body number, indicating that only 14% of the bodies of all passengers who perished were recovered.

*This is neither an error nor a missing value - there were passengers on the Titanic with free tickets (funded by the carrier). We will discuss free tickets on the Titanic during the analysis of the outliers.

5. Analysis of Relationships Between Variables.

At this stage, we will focus on examining the relationships between variables. Initially, we will investigate the survival chances of passengers. We will try to determine which variables played the most significant role in the fight for survival. Do survival chances depend on age, gender, wealth, or whether someone traveled alone or with family? We will also discuss other interesting topics. Below is a list of questions I intend to answer:

1. How many men and women were there in relation to how many men and women survived the disaster?
2. How many men and women were there in relation to how many men and women survived the disaster, broken down by class?
3. How many children (<18) were there in each class - how many children survived in each class?
4. How many elderly people (60+) were there in each class - how many 60+ people survived in each class?
5. Did passengers who paid more for their tickets have a higher chance of survival, considering the class breakdown?
6. Did passengers traveling with family have a higher chance of survival than those traveling alone, considering the class breakdown, and is there a correlation for men who have the lowest survival rate?
7. Distribution of people in lifeboats, considering the class breakdown. Were first-class passengers privileged - did they have their own lifeboats and more space?

1. How many men and women were there in relation to how many men and women survived the disaster?

	Total	Survived	Total_Percentage	Survived_Percentage
sex
female	466	339	35.599694	67.800000
male	843	161	64.400306	32.200000

2. How many men and women were there in each class - how many men and women survived the disaster?

Count Type	Total		Survived		Survival Percentage
Sex	female	male	female	male	female	male
pclass
1.000000	144	179	139	61	96.527778	34.078212
2.000000	106	171	94	25	88.679245	14.619883
3.000000	216	493	106	75	49.074074	15.212982

3. How many children were there in each class - how many children survived in each class?

	Total Children	Survived	Survived (%)
pclass
1.000000	15	13	86.666667
2.000000	33	29	87.878788
3.000000	106	39	36.792453

4. How many elderly people (50+) were there in each class - how many elderly people (50+) survived in each class?

	Total Elders	Survived	Survived (%)
pclass
1.000000	64	34	53.125000
2.000000	20	3	15.000000
3.000000	11	1	9.090909

5. Did passengers who paid more for their tickets have a higher chance of survival?

	Class	Top 20% Survived	Top 20% Total	Top 20% Survival Rate (%)	Bottom 20% Survived	Bottom 20% Total	Bottom 20% Survival Rate (%)
0	1.000000	45.000000	65	69.230769	31.000000	69	44.927536
1	2.000000	36.000000	60	60.000000	15.000000	56	26.785714
2	3.000000	38.000000	147	25.850340	50.000000	196	25.510204

6. Survival rates of passengers with and without family, broken down by class. Survival rates of men with family

	Class	Survived_Alone	Survived_With_Family	Total_Alone	Total_With_Family	Survival_Rate_Alone	Survival_Rate_With_Family	Male_Survived_Alone	Male_Survived_With_Family	Male_Total_Alone	Male_Total_With_Family	Male_Survival_Rate_Alone	Male_Survival_Rate_With_Family
0	1.000000	82.000000	118.000000	160.000000	163.000000	51.250000	72.392638	32.000000	29.000000	108.000000	71.000000	29.629630	40.845070
1	2.000000	48.000000	71.000000	158.000000	119.000000	30.379747	59.663866	12.000000	13.000000	116.000000	55.000000	10.344828	23.636364
2	3.000000	109.000000	72.000000	472.000000	237.000000	23.093220	30.379747	53.000000	22.000000	372.000000	121.000000	14.247312	18.181818

7. Distribution of people in lifeboats with a breakdown by class. Were first-class passengers privileged - did they have their own lifeboats and more space?

pclass	1.000000	2.000000	3.000000
boat
1	5	0	0
10	8	15	6
11	6	14	5
12	0	17	2
13	1	12	29
14	5	23	5
15	1	1	36
16	0	3	20
2	7	0	6
3	26	0	0
4	24	7	0
5	30	0	0
6	19	0	1
7	22	1	0
8	24	0	0
9	6	16	3
A	3	0	8
B	3	1	5
C	2	0	38
D	9	2	9

Summary of the Analysis of Relationships Between Variables

1. There were 466 women and 843 men on board. 67% (339) of women survived the disaster, compared to only 32% (161) of men.
2. In the first class, 96% of women and 34% of men survived. In the second class, 88% of women and only 14% of men survived. In the third class, only 49% of women and 15% of men survived.
3. In the first class, 86% of children under 18 survived. In the second class, 87% survived, and in the third class, only 36% survived.
4. In the first class, 53% of passengers aged 50+ survived. In the second class, 15% survived, and in the third class, 9% survived.
5. Passengers in the 1st and 2nd class who paid more for their tickets had a better chance of survival. The ticket price in the 3rd class did not affect survival rates.
6. Traveling with family had a positive impact on survival rates. This trend is visible among 1st and 2nd class passengers, likely because wealthier women, who had the highest survival rates, typically did not travel alone at that time. I specifically highlighted men to see if those traveling with and caring for their families had a better chance of survival than those traveling alone. According to my chart above, it seems they did not. However, ChatGPT, citing articles on the disaster, claims they definitely did.
7. Officially, there was no class division during evacuation. Unofficially, first-class passengers were highly privileged compared to others. This was due to their cabins being closest to the lifeboats and the crew assisting first-class passengers first. First-class passengers were also the first to be informed by the crew about the severity of the situation. This information was crucial, as most passengers realized too late that the unsinkable Titanic would indeed sink. The chart above shows that boats 3-8 were almost exclusively filled with first-class passengers. Our DataFrame does not contain crew data, but we know from other sources that lifeboats were not full. This was likely to ensure more comfort for first-class passengers. The chaos and panic during evacuation and lack of proper crew training were contributing factors.

Source: ChatGPT, when asked for sources, cites the books: "A Night to Remember" by Walter Lord, "The Loss of the S.S. Titanic" by Lawrence Beesley, "Titanic: A Voyage of Discovery" by Tror Rowe. It's interesting to consider whether it actually has these books in its datasets or just claims to for appearance.

6. Analysis of Outliers

At this stage, we will examine values and information that significantly differ from the rest of the data. We will answer the following questions:

1. How many small children under 2 (outliers) were there, and what was their survival rate?
2. Who were the individuals with the most expensive tickets, and what was included in the ticket price that made it almost 100 times more expensive than the cheapest?
3. How many people had free tickets, why, and who were they?
4. Why were there only 5 passengers in lifeboat number one, and who were they?
5. Were there individuals who made it to a lifeboat but did not survive?
6. Were there individuals who did not make it to any lifeboat but somehow survived?

1. How many small children under 2 (outliers) were there, and what was their survival rate?

	Total Children	Survived	Survived (%)
pclass
1.000000	1	1	100.000000
2.000000	7	7	100.000000
3.000000	14	9	64.285714

2. Who were the people who had the most expensive tickets, and what was included in the ticket price that made it almost 200 times more expensive than the cheapest one?

		Total Rich	Survived	Survived (%)
pclass	sex
1.000000	female	53	50	94.339623
	male	31	10	32.258065

3. Who were the people who had the most expensive tickets, and what was included in the ticket price that made it almost 200 times more expensive than the cheapest one?

	name	survived	sex	age	pclass	with family	fare	boat	body
49	Cardeza, Mr. Thomas Drake Martinez	1.000000	male	36.000000	1.000000	1.000000	512.329200	3	nan
50	Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)	1.000000	female	58.000000	1.000000	1.000000	512.329200	3	nan
183	Lesurer, Mr. Gustave J	1.000000	male	35.000000	1.000000	0.000000	512.329200	3	nan
302	Ward, Miss. Anna	1.000000	female	35.000000	1.000000	0.000000	512.329200	3	nan

3. How many people had a free ticket? Why? Who were they?

	name	survived	sex	age	pclass	with family	fare	boat	body
1	Andrews, Mr. Thomas Jr	0.000000	male	39.000000	1.000000	0.000000	0.000000	nan	nan
2	Chisholm, Mr. Roderick Robert Crispin	0.000000	male	28.000000	1.000000	0.000000	0.000000	nan	nan
3	Fry, Mr. Richard	0.000000	male	28.000000	1.000000	0.000000	0.000000	nan	nan
4	Harrison, Mr. William	0.000000	male	40.000000	1.000000	0.000000	0.000000	nan	110.000000
5	Ismay, Mr. Joseph Bruce	1.000000	male	49.000000	1.000000	0.000000	0.000000	C	nan
6	Parr, Mr. William Henry Marsh	0.000000	male	28.000000	1.000000	0.000000	0.000000	nan	nan
7	Reuchlin, Jonkheer. John George	0.000000	male	38.000000	1.000000	0.000000	0.000000	nan	nan
8	Campbell, Mr. William	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
9	Cunningham, Mr. Alfred Fleming	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
10	Frost, Mr. Anthony Wood "Archie"	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
11	Knight, Mr. Robert J	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
12	Parkes, Mr. Francis "Frank"	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
13	Watson, Mr. Ennis Hastings	0.000000	male	28.000000	2.000000	0.000000	0.000000	nan	nan
14	Johnson, Mr. Alfred	0.000000	male	49.000000	3.000000	0.000000	0.000000	nan	nan
15	Johnson, Mr. William Cahoone Jr	0.000000	male	19.000000	3.000000	0.000000	0.000000	nan	nan
16	Leonard, Mr. Lionel	0.000000	male	36.000000	3.000000	0.000000	0.000000	nan	nan
17	Tornquist, Mr. William Henry	1.000000	male	25.000000	3.000000	0.000000	0.000000	15	nan

4. Why were there only 5 passengers in lifeboat number one and who were they?

	name	survived	sex	age	pclass	with family	fare	boat	body
1	Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan")	1.000000	female	48.000000	1.000000	1.000000	39.600000	1	nan
2	Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")	1.000000	male	49.000000	1.000000	1.000000	56.929200	1	nan
3	Francatelli, Miss. Laura Mabel	1.000000	female	30.000000	1.000000	0.000000	56.929200	1	nan
4	Salomon, Mr. Abraham L	1.000000	male	28.000000	1.000000	0.000000	26.000000	1	nan
5	Stengel, Mr. Charles Emil Henry	1.000000	male	54.000000	1.000000	1.000000	55.441700	1	nan

5. Were there people who got on a lifeboat but did not survive?

	name	survived	sex	age	pclass	with family	fare	boat	body
1	Beattie, Mr. Thomson	0.000000	male	36.000000	1.000000	0.000000	75.241700	A	nan
2	Hoyt, Mr. William Fisher	0.000000	male	28.000000	1.000000	0.000000	30.695800	14	nan
3	Renouf, Mr. Peter Henry	0.000000	male	34.000000	2.000000	1.000000	21.000000	12	nan
4	Backstrom, Mr. Karl Alfred	0.000000	male	32.000000	3.000000	1.000000	15.850000	D	nan
5	Harmer, Mr. Abraham (David Lishin)	0.000000	male	25.000000	3.000000	0.000000	7.250000	B	nan
6	Keefe, Mr. Arthur	0.000000	male	28.000000	3.000000	0.000000	7.250000	A	nan
7	Lindell, Mr. Edvard Bengtsson	0.000000	male	36.000000	3.000000	1.000000	15.550000	A	nan
8	Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)	0.000000	female	30.000000	3.000000	1.000000	15.550000	A	nan
9	Yasbeck, Mr. Antoni	0.000000	male	27.000000	3.000000	1.000000	14.454200	C	nan

6. Were there people who did not get on any lifeboat but somehow survived?

	name	survived	sex	age	pclass	with family	fare	boat	body
1	Lurette, Miss. Elise	1.000000	female	58.000000	1.000000	0.000000	146.520800	nan	nan
2	Bystrom, Mrs. (Karolina)	1.000000	female	42.000000	2.000000	0.000000	13.000000	nan	nan
3	Doling, Miss. Elsie	1.000000	female	18.000000	2.000000	1.000000	23.000000	nan	nan
4	Doling, Mrs. John T (Ada Julia Bone)	1.000000	female	34.000000	2.000000	1.000000	23.000000	nan	nan
5	Ilett, Miss. Bertha	1.000000	female	17.000000	2.000000	0.000000	10.500000	nan	nan
6	Louch, Mrs. Charles Alexander (Alice Adelaide Slow)	1.000000	female	42.000000	2.000000	1.000000	26.000000	nan	nan
7	Nasser, Mrs. Nicholas (Adele Achem)	1.000000	female	14.000000	2.000000	1.000000	30.070800	nan	nan
8	Renouf, Mrs. Peter Henry (Lillian Jefferys)	1.000000	female	30.000000	2.000000	1.000000	21.000000	nan	nan
9	Trout, Mrs. William H (Jessie L)	1.000000	female	28.000000	2.000000	0.000000	12.650000	nan	nan
10	Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)	1.000000	female	33.000000	3.000000	1.000000	15.850000	nan	nan
11	Drapkin, Miss. Jennie	1.000000	female	23.000000	3.000000	0.000000	8.050000	nan	nan
12	Heikkinen, Miss. Laina	1.000000	female	26.000000	3.000000	0.000000	7.925000	nan	nan
13	Honkanen, Miss. Eliina	1.000000	female	27.000000	3.000000	0.000000	7.925000	nan	nan
14	Kennedy, Mr. John	1.000000	male	28.000000	3.000000	0.000000	7.750000	nan	nan
15	McCormack, Mr. Thomas Joseph	1.000000	male	28.000000	3.000000	0.000000	7.750000	nan	nan
16	McGowan, Miss. Anna "Annie"	1.000000	female	15.000000	3.000000	0.000000	8.029200	nan	nan
17	Moussa, Mrs. (Mantoura Boulos)	1.000000	female	28.000000	3.000000	0.000000	7.229200	nan	nan
18	O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey)	1.000000	female	28.000000	3.000000	1.000000	15.500000	nan	nan
19	O'Dwyer, Miss. Ellen "Nellie"	1.000000	female	28.000000	3.000000	0.000000	7.879200	nan	nan
20	Osman, Mrs. Mara	1.000000	female	31.000000	3.000000	0.000000	8.683300	nan	nan
21	Shine, Miss. Ellen Natalia	1.000000	female	28.000000	3.000000	0.000000	7.779200	nan	nan
22	Wilkes, Mrs. James (Ellen Needs)	1.000000	female	47.000000	3.000000	1.000000	7.000000	nan	nan
23	Yasbeck, Mrs. Antoni (Selini Alexander)	1.000000	female	15.000000	3.000000	1.000000	14.454200	nan	nan

Summary of Outlier Analysis

1. There were a total of 22 small children under the age of 2 on the Titanic. Five of them did not survive. They traveled in 3rd class. Being in a privileged group was not enough to survive in 3rd class. The 3rd class had difficulty accessing lifeboats because a large part of the ship was inaccessible to them and was barred by gates.
2. The most expensive ticket on the Titanic cost 512 GBP. This was 36 times the median price of all tickets. The chart above illustrates the vast gap between the wealthiest and the average passengers. It was an astronomical sum for those times. For comparison, a house in England could be bought for 250 GBP. The price of the most expensive ticket included a "Parlor Suite" apartment with two bedrooms and a private patio. According to our list, only four people could afford such luxury. Additionally, we know that the rest of the 'Parlor Suite' apartments were not for sale but were reserved for the line's owners and VIP guests for promotional purposes. As it turned out, room service, electric blankets and pillows, and access to the gym were not as significant an advantage as the fact that these apartments were on the same deck as the lifeboats.
3. According to the dataset, 17 people traveled on the Titanic for free. This is not a data error. Some passengers indeed did not pay for their tickets. In the first class, this included the owner of the White Star Lines - Mr. Joseph Bruce Ismay, and his colleagues and close friends (they occupied the remaining apartments described in the previous point). In the 2nd and 3rd classes, these were mainly contractors and employees of the line who were not part of the permanent crew - including members of the famous orchestra that played until the end.
4. Lifeboat number one carried only five passengers. This is not a data error. First-class passengers, the Duff Gordons, and three of their friends escaped the Titanic by organizing a private lifeboat. Sir Cosmo Duff Gordon was even charged with bribing the crew and refusing to help others but was later acquitted of the charges.
5. There are nine people who have a lifeboat number but did not survive. This is not a data error. Sources tell us that the boat marked A partially took on water, and passengers sat in it knee-deep in icy water. As for the remaining five people, we are not sure. It can be assumed that hypothermia played a significant role.
6. There are 23 people who are not assigned to any boat but survived nonetheless. However, I suspect a data error here. Most of these people are young women from the 2nd and 3rd classes. I have not found any information that could confirm or deny this, but I suspect an error - such as not providing a boat number.

Source: When asked about the source, ChatGPT cites books: "A Night to Remember" by Walter Lord, "The Loss of the S.S. Titanic" by Lawrence Beesley, "Titanic: A Voyage of Discovery" by Trev Rowe. It's interesting whether it actually has these books in its datasets or just claims so to 'look better.'

Observations and Final Conclusions

Finally, I added two charts that clearly illustrate the survival correlations. Yellow dots represent rescued individuals. Large dots indicate first class, medium dots indicate second class, and the smallest dots indicate third class.

What do the numbers tell us?

If we were to identify a single variable that had the greatest impact on survival, it was undoubtedly gender. The crew followed the principle of women and children first. This rule was imposed by both maritime law and custom. This is clearly reflected in the data. The second most important factor was class and ticket price - wealthier passengers were more privileged. The third factor was age, meaning that the youngest passengers had the highest chances of survival. The fourth factor was whether someone traveled with family or alone. However, this variable played a lesser role for the poorest passengers.

The data on class and ticket price perfectly reflect the social stratification of that time. The majority of victims were men traveling in third class. In this class, fatalities were even seen among the youngest passengers. The poorest passengers were isolated from the upper decks of the massive ship. They were also isolated from information. A large portion of passengers simply did not know they were in serious trouble. Even if there was space for them in the lifeboats, they either couldn't reach them or didn't know they should.

Could more people have been saved? The data suggests yes. There was still plenty of room in the lifeboats. However, this would not have significantly changed the scale of the tragedy. There were simply too few lifeboats. I believe we should not judge the decisions made during the evacuation. None of us knows how we would have acted. We should judge the decisions made "calmly" on an ordinary day in the comfort of one's office. These very decisions led to so many deaths. Evidence of this is the fact that after the disaster, a number of legal regulations regarding the number of lifeboats and navigation and speed in difficult sea conditions were changed.

The biggest problem with the Titanic was not the lack of crew training, the chaotic and inept evacuation, the lack of lifeboats, or excessive speed. The biggest problem was that everyone - absolutely everyone - from the poorest to the wealthiest succumbed to the illusion that the Titanic was unsinkable. This illusion led to a series of poor decisions before the disaster and to panic and chaos during the disaster. The belief in one's own infallibility, arrogance, and blind faith in technology - these were the main causes of this epic tragedy. I wonder if things are different today.

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